Chen, Chang; Zhang, Chao; Zhao, Lihu; Yang, Cuicui; Yao, Xiaochuang; Fu, Bin
Abstract
Accurate crop spatial distribution data are a fundamental basis for optimizing cropping structures and enhancing grain production. Timely and precise crop classification mapping is essential for effective agricultural monitoring. This study proposes a multi-module remote sensing classification model, the GEE-Deep-Crop-Mapping model (GDCM), constructed on the basis of multi-source remote sensing data from the Google Earth Engine (GEE) platform. The GDCM consists of two main modules: a multi-scale spatial module integrating dilated and depthwise separable convolutions with CBAM to enhance spectral-textural features across varying field sizes; A temporal dynamics module combining Bi-LSTM and multi-head attention to capture phenological dependencies and identify critical growth phases. The GDCM is Applied to Nenjiang City, Heilongjiang province, China with Sentinel-1 and Sentinel-2 time-series data, the GDCM model achieved over 95% accuracy, outperforming both the Bi-LSTM and Random Forest (RF) models. Similarly, the model achieved 90% accuracy in Cass County, USA. Additionally, by leveraging transfer learning, the optimized model successfully extracted multi-year crop distribution data, facilitating the monitoring and analysis of grain-soybean rotation patterns from 2017 to 2022 in Nenjiang City. These findings highlight the potential of the GDCM model for advancing agricultural monitoring and optimizing cropping structures.
Soybean cultivation and crop rotation monitoring based on multi-source remote sensing data and Bi-LSTM enhanced model.pdf